Ekkono
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About
Ekkono provides edge AI solutions designed for deployment on resource-constrained devices. It focuses on sensor data analysis, condition monitoring, performance optimization and sustainability. Their software supports incremental learning, real-time data preprocessing, and anomaly detection. They offer a Software Development Kit (SDK) for model generation, optimization, evaluation and implementation, and support model life-cycle management (MLCM).
Platform
Task
Features
• performance optimization
• condition monitoring
• edge ai
• condition-based maintenance
• ai on everything
• adaptive automation
• sustainable products embedded edge ai
• individual condition monitoring
FAQs
How is this different from TensorFlow Lite/Micro?
TensorFlow Lite/Micro is designed for deep learning tasks like image classification on edge devices. However, the vast majority of edge ML use cases focus on sensor data—such as pressure, temperature, or power consumption—rather than images. These scenarios require functionality like incremental learning, real-time data preprocessing and anomaly detection, which TensorFlow Lite/Micro lacks.
How we verify that a model learns correctly
Machine learning models are inherently designed to filter out noise and disturbances in data, so minor variations are typically disregarded without requiring additional measures. To ensure a model is learning correctly, Ekkono provides several built-in tools: 1. Track Error Over Time: Ekkono monitors error trends to confirm learning. A steady decrease in error indicates effective training. 2. Pause Training: Temporarily disable training to see if the model maintains performance, confirming it has learned relevant patterns. 3. Handle Sensor Errors: Systematic sensor errors should be addressed at the IoT platform level. Alternatively, Ekkono can monitor sensors individually using change detection. These features ensure reliable learning without the need for extra error-checking functionality.
Pricing Plans
Job Opportunities
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